This is a template of interactive reporting output generated by Saldae Analytics Platform. If you want to learn more about us and our service please visit our website:
https://www.fairanalytics.net/
Total Sales of Beers in the following cities next month :
Forecasting:
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning
1961.833
1627.412
---
title: Saldae Analytics Q1
author: Anis
date: 2022-06-11 13:56
output:
flexdashboard::flex_dashboard:
orientation: columns
social: menu
theme: bootstrap
mathjax: ~
favicon: saldae_logo.png
source_code: embed
params:
sald_explor_chart: NA
sald_predict_chart: NA
sald_tisefka: NA
sald_predict_values: NA
sald_predict_comment: NA
sald_introduction: NA
sald_report_asezwer: NA
---
# Saldae {data-navmenu="Intro"}
Row {data-width=200}
-----------------------------------------------------------------------
### Analysis Summary {data-commentary-height=200}
```{r,echo=FALSE,warning=FALSE,results='asis'}
h4(params$sald_report_asezwer)
```
***
```{r,echo=FALSE,warning=FALSE}
future_key_figures <- unlist(params$sald_predict_values$key_figures%>%purrr::map(~.x[[1]]))
future_key_figures <- data.frame(attribute = names(future_key_figures),forecast =future_key_figures)
```
Row {data-width=500}
-----------------------------------------------------------------------
### Key Figures ( Prediction)
```{r,echo=FALSE,warning=FALSE}
DT::datatable(future_key_figures,
extensions = c('Buttons','Scroller'), options = list(
deferRender = TRUE,
scrollY = 200,
scroller = TRUE,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
)
)
```
### Key figures (contribution)
```{r,echo=FALSE,warning=FALSE}
future_key_figures_pie <- future_key_figures%>%plotly::plot_ly( labels = ~attribute, values= ~forecast, type = 'pie')
future_key_figures_pie
```
Row
-----------------------------------------------------------------------
### Intro {data-commentary-height=400}
This is a template of interactive reporting output generated by [Saldae Analytics Platform](https://saldae-analytics.shinyapps.io/saldae-analytics-platform/).
If you want to learn more about us and our service please visit our website:
[https://www.fairanalytics.net/](https://www.fairanalytics.net/)
*Total Sales of Beers in the following cities next month :*
```{r,echo=FALSE,warning=FALSE}
future_key_figures_bar <- future_key_figures%>%plotly::plot_ly( y = ~attribute, x = ~forecast, type = 'bar',color = ~attribute, orientation = 'h')
future_key_figures_bar
```
***
### Useful concepts {data-commentary-height=300}
**Forecasting:**
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning
***
`r names(params$sald_explor_chart)[[1]]`
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### `r paste(names(params$sald_explor_chart)[[1]] ,":",params$sald_predict_values$key_metric)`
```{r}
my_key_figure <- params$sald_predict_values$key_figures[[1]]
flexdashboard::valueBox(my_key_figure,color = "brown", icon = "fa-euro-sign")
```
### Summary {data-commentary-height=100}
```{r,warning=FALSE,echo=FALSE}
h5(params$sald_predict_comment[[1]])
```
***
Row {.tabset}
-----------------------------------------------------------------------
### Forecast Chart
```{r, warning=FALSE,fig.height=6,echo=FALSE}
params$sald_explor_chart[[1]]
```
### Forecast Table
```{r, warning=FALSE,fig.height=4,echo=FALSE}
DT::datatable(params$sald_predict_chart[[1]],
extensions = c('Buttons','Scroller'), options = list(
deferRender = TRUE,
scrollY = 400,
scroller = TRUE,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
)
)
```
### Anomalies
```{r ,warning=FALSE,fig.height=4,message=FALSE,echo=FALSE}
tisefka <- params$sald_tisefka%>%select(date,names(params$sald_explor_chart)[1])
tisefka2 <<- tisefka
tisefka%>%
SaldaeDataExplorer::anomaly_detection_yiwen()%>%
SaldaeDataExplorer::SA_anomaly_charter(target_variable = names(params$sald_explor_chart)[1])
```
`r names(params$sald_explor_chart)[[2]]`
=======================================================================
Row {data-width=200}
-----------------------------------------------------------------------
### `r paste(names(params$sald_explor_chart)[[2]] ,":",params$sald_predict_values$key_metric)`
```{r}
my_key_figure <- params$sald_predict_values$key_figures[[2]]
flexdashboard::valueBox(my_key_figure,color = "brown", icon = "fa-euro-sign")
```
### Summary {data-commentary-height=100}
```{r,warning=FALSE,echo=FALSE}
h5(params$sald_predict_comment[[2]])
```
***
Row {.tabset}
-----------------------------------------------------------------------
### Forecast Chart
```{r, warning=FALSE,fig.height=6,echo=FALSE}
params$sald_explor_chart[[2]]
```
### Forecast Table
```{r, warning=FALSE,fig.height=4,echo=FALSE}
DT::datatable(params$sald_predict_chart[[2]],
extensions = c('Buttons','Scroller'), options = list(
deferRender = TRUE,
scrollY = 400,
scroller = TRUE,
dom = 'Bfrtip',
buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
)
)
```
### Anomalies
```{r ,warning=FALSE,fig.height=4,message=FALSE,echo=FALSE}
tisefka <- params$sald_tisefka%>%select(date,names(params$sald_explor_chart)[2])
tisefka2 <<- tisefka
tisefka%>%
SaldaeDataExplorer::anomaly_detection_yiwen()%>%
SaldaeDataExplorer::SA_anomaly_charter(target_variable = names(params$sald_explor_chart)[2])
```